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On a hybrid data cloning method and its application in generalized linear mixed models

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Abstract

The data cloning method is a new computational tool for computing maximum likelihood estimates in complex statistical models such as mixed models. This method is synthesized with integrated nested Laplace approximation to compute maximum likelihood estimates efficiently via a fast implementation in generalized linear mixed models. Asymptotic behavior of the hybrid data cloning method is discussed. The performance of the proposed method is illustrated through a simulation study and real examples. It is shown that the proposed method performs well and rightly justifies the theory. Supplemental materials for this article are available online.

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Authors and Affiliations

  1. Department of Statistics, Tarbiat Modares University, Tehran, Iran

    Hossein Baghishani & Mohsen Mohammadzadeh

  2. The Norwegian University of Science and Technology, Trondheim, Norway

    Håvard Rue

Authors
  1. Hossein Baghishani
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  2. Håvard Rue
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  3. Mohsen Mohammadzadeh
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Corresponding author

Correspondence to Hossein Baghishani.

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Baghishani, H., Rue, H. & Mohammadzadeh, M. On a hybrid data cloning method and its application in generalized linear mixed models. Stat Comput 22, 597–613 (2012). https://doi.org/10.1007/s11222-011-9254-z

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  • DOI: https://doi.org/10.1007/s11222-011-9254-z

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